Let's see if Claude is good at this

app.py

@@ -1742,15 +1742,20 @@ if selected_tab == 'Data Load':
 
 if selected_tab == 'Manage Portfolio':
     if 'base_frame_names' not in st.session_state:
-            st.session_state['base_frame_names'] = {'Default': None}
+            st.session_state['base_frame_names'] = {}
     if 'origin_portfolio' in st.session_state and 'projections_df' in st.session_state:
         with st.container():
             reset_port_col, recalc_div_col, set_base_col, blank_reset_col, contest_size_col = st.columns([1, 1, 1, 6, 4])
             with reset_port_col:
                 with st.popover("Reset Portfolio"):
                     st.markdown("choose a base to reset to:")
-                    base_choice = st.selectbox("Base Choice", options=st.session_state['base_frame_names'], index=0)
-                    st.session_state['working_frame'] = st.session_state['base_frame_names'][base_choice]
+                    if st.session_state['base_frame_names']:
+                        base_choice = st.selectbox("Base Choice", options=list(st.session_state['base_frame_names'].keys()), index=0)
+                        if st.button("Load Selected Base"):
+                            st.session_state['working_frame'] = st.session_state['base_frame_names'][base_choice].copy()
+                            st.rerun()
+                    else:
+                        st.info("No saved base frames available")
             with recalc_div_col:
                 if st.button("Recalculate Diversity"):
                         st.session_state['display_frame']['Diversity'] = recalc_diversity(st.session_state['display_frame'], st.session_state['player_columns'])
@@ -1758,12 +1763,14 @@ if selected_tab == 'Manage Portfolio':
                 with st.popover("New Base Setting"):
                     st.markdown("Name of new base:")
                     new_base_name = st.text_input("New Base Name", value='New Base')
-                    if f'{new_base_name}' not in st.session_state['base_frame_names']:
-                        st.session_state['base_frame_names'].append(f'{new_base_name}')
-                        st.session_state['base_frame_names'] = sorted(st.session_state['base_frame_names'])
-                        st.session_state['base_frame_names'][new_base_name] = st.session_state['working_frame']
-                    else:
-                        st.error("Base name already exists")
+                    if st.button("Save Current as Base"):
+                        if new_base_name and new_base_name not in st.session_state['base_frame_names']:
+                            st.session_state['base_frame_names'][new_base_name] = st.session_state['working_frame'].copy()
+                            st.success(f"Base '{new_base_name}' saved successfully!")
+                        elif new_base_name in st.session_state['base_frame_names']:
+                            st.error("Base name already exists")
+                        else:
+                            st.error("Please enter a base name")
 
             with contest_size_col:
                 with st.form(key='contest_size_form'):
@@ -1775,35 +1782,42 @@ if selected_tab == 'Manage Portfolio':
                     with submit_col:
                         submitted = st.form_submit_button("Submit Size/Strength")
                     if submitted:
-                        del st.session_state['working_frame']
+                        if 'working_frame' in st.session_state:
+                            del st.session_state['working_frame']
         
         excluded_cols = ['salary', 'median', 'Own', 'Finish_percentile', 'Dupes', 'Stack', 'Size', 'Win%', 'Lineup Edge', 'Lineup Edge_Raw', 'Weighted Own', 'Geomean', 'Diversity', 'SE Score']
         
 
         if 'working_frame' not in st.session_state:
             st.session_state['settings_base'] = True
-            st.session_state['working_frame'] = pd.read_parquet(io.BytesIO(st.session_state['origin_portfolio']))
-            st.session_state['player_columns'] = [col for col in st.session_state['working_frame'].columns if col not in excluded_cols]
+            
+            # Load and process the origin portfolio
+            initial_frame = pd.read_parquet(io.BytesIO(st.session_state['origin_portfolio']))
+            st.session_state['player_columns'] = [col for col in initial_frame.columns if col not in excluded_cols]
             
             # Use vectorized calculation function
-            st.session_state['working_frame'] = calculate_lineup_metrics(
-                st.session_state['working_frame'], 
+            processed_frame = calculate_lineup_metrics(
+                initial_frame, 
                 st.session_state['player_columns'], 
                 st.session_state['map_dict'], 
                 type_var, 
                 sport_var, 
                 st.session_state['projections_df'] if 'stack_dict' in st.session_state else None
             )
-            st.session_state['working_frame'] = st.session_state['working_frame'][st.session_state['working_frame']['salary'] <= salary_max]
+            processed_frame = processed_frame[processed_frame['salary'] <= salary_max]
             
             # Map existing stack/size data if available
             if 'stack_dict' in st.session_state:
-                st.session_state['working_frame']['Stack'] = st.session_state['working_frame'].index.map(st.session_state['stack_dict'])
-                st.session_state['working_frame']['Size'] = st.session_state['working_frame'].index.map(st.session_state['size_dict'])
+                processed_frame['Stack'] = processed_frame.index.map(st.session_state['stack_dict'])
+                processed_frame['Size'] = processed_frame.index.map(st.session_state['size_dict'])
+            
+            # Create the final base frame with dupe predictions
+            final_base_frame = predict_dupes(processed_frame, st.session_state['map_dict'], site_var, type_var, Contest_Size, strength_var, sport_var, salary_max)
+            
+            # Set up the Default base and working frame
+            st.session_state['base_frame_names']['Default'] = final_base_frame.copy()
+            st.session_state['working_frame'] = final_base_frame.copy()
             
-            st.session_state['base_frame'] = predict_dupes(st.session_state['working_frame'], st.session_state['map_dict'], site_var, type_var, Contest_Size, strength_var, sport_var, salary_max)
-            st.session_state['base_frame_names']['Default'] = st.session_state['base_frame']
-            st.session_state['working_frame'] = st.session_state['base_frame_names']['Default']
             # st.session_state['highest_owned_teams'] = st.session_state['projections_df'][~st.session_state['projections_df']['position'].isin(['P', 'SP'])].groupby('team')['ownership'].sum().sort_values(ascending=False).head(3).index.tolist()
             # st.session_state['highest_owned_pitchers'] = st.session_state['projections_df'][st.session_state['projections_df']['position'].isin(['P', 'SP'])]['player_names'].sort_values(by='ownership', ascending=False).head(3).tolist()